ABSTRACT Due to the advantages of low‐voltage operation and memory characteristics, electrolyte‐gated transistors (EGTs) have been proposed as building blocks for neuromorphic systems. Recently, reservoir computing (RC) is attracting interests because RC utilize nonlinear system to enrich the input source into a higher dimension, whereas traditional neural network prefers linear system. RC also has the advantages of low energy consumption and fast response speed because it only trains at the readout layer. EGT is a promising candidate for RC due to tunable output current depending on input pulse voltage using its nonlinear system. Herein, we proposed poly (vinylidene fluoride‐hexafluoropropylene) (PVDF‐HFP) interlayered EGT for RC. Semicrystalline and porous morphology of PVDF‐HFP interlayer induced ion‐trapping at the interface between amorphous and crystalline regions resulted in long retention. Also, the thickness variation of the PVDF‐HFP interlayer modulates the filling capacity of trap sites, balancing ion‐transport for near‐linear weight update. We integrated near‐linear and asymmetric long‐term potentiation and depression curves into RC to elucidate the intensity difference between five frames of spatiotemporal events in dynamic vision sensor (DVS) gestures. Classification of 11 distinct gestures was successfully demonstrated using the RC framework. This work highlights compelling synergy between material‐level device physics and algorithmic architectures of neuromorphic computing.
Kim et al. (Wed,) studied this question.